Remote Sensing Reflectance and Derived Products: MODIS and VIIRS

MODIS/VIIRS Science Team, 15-19 October 2018, Silver Spring, MD

Bryan FranzOcean Ecology Laboratory

NASA Goddard Space Flight Center

Project Team

MODIS Algorithm MaintenanceRemote Sensing Reflectance, Chlorophyll, Diffuse Attenuation

Science of Terra/Aqua/SNPPAdvancingtheQualityandContinuityofMarineRemoteSensingReflectanceandDerivedOceanColorProductsfromMODIStoVIIRS

TeamMember RoleBryanFranz(PI) ProjectleadAmirIbrahim(Co-I) AtmosphericcorrectionSeanBailey(Co-I) VicariouscalibrationGerhardMeister(Co-I) InstrumentcalibrationJeremyWerdell (Co-I) Bio-opticalalgorithmsZiaAhmad(Supp) AC&radiativetransfercodeGeneEplee (Supp) Instrumentcalibration(VIIRS)Shihyan Lee(Supp) Instrumentcalibration(MODIS)ChrisProctor(Supp) InsituvalidationErdem Karakoylu (Supp) Uncertainties

andtheOceanBiologyProcessingGroup

Contents

1. StatusofMODIS&VIIRSOceanColorProducts

2. StatusandPlansforAlgorithmAdvancement

1. Multi-bandAtmosphericCorrection

2. Rrs UncertaintyProduct

Ocean Color Reprocessing R2018.0

Completed• Dec2017(VIIRS/SNPP),January2018(MODIS/Aqua),April2018(MODIS/Terra)

Purpose• incorporateupdatestovicariouscalibrationduetorevisedMOBYtime-series• incorporateupdatestoinstrumentcalibration• noalgorithmchanges

Impact of MOBY Revisions on MODISA Ocean ColorGlobal Deep Water Time-Series Test (MOBY change only)

MOBYinsituradiometryisusedforvicariouscal ofvisiblebands,allmissions

MOBYreprocessedbyMOTin2016and2017• reviseddepthofsensorarms• newstraylightcorrection

Impactwasto• increaseRrs by2-10%• decreaseglobalChl by8%

RrsR

atio

Rrs(sr

-1)

ChlRatio

Chl(mgm

-3)

MOBYbiasshift+2-10%inRrs

MOBYbiasshift-8%inChl

MODISA Instrument Calibration UpdateGoalwastomitigatesometemporalartifactsobservedinpreviousoceancolortrends,believedtobeassociatedwithcal datasmoothing/fittingdecisions.

Completeend-to-endre-analysisofon-boardcalibrationwasperformed,largelyfollowingMCSTapproach,butwithsomedifferences.• SD/SDSMscreens,SDBRF:derivedfromsimplefitofyawmaneuverdata,notgeometricmodeling

• SDSMCal:SDdegradationsinred/NIRweredeterminedbywavelengthmodeling(Leeetal.,2018)

• RVS:simpleatmosphericcorrectionaddedwhencomputingdeserttrendstotrackRVS in412,443nm

• ModulatedRSR:impactonoceandataestimatedandusedtoadjust412nmtemporalgains(Leeet.al.2017)

• RSBLUTs:useofsmoothingratherthanfittingtotheinstrumenttemporalcalibrationtrendingand

characterization(i.e.,noassumptiononfunctionalform)

Monthlyupdatetomaintaincalibrationtrendconsistencyinforwardprocessing.

MODISA Global Deep-Water (R2018.0 vs R2014.0)

RrsR

atio

Rrs(sr

-1)

ChlRatio

Chl(mgm

-3)

instrumentcalibrationupdate

MOBYbiasshift+2-10%inRrs

MOBYbiasshift-8%inChl

Late-missiondriftinblueRrs resolved.

Largeseasonalspikesinchlorophyllremoved.

MODISA Rrs Anomaly Trend in Global Oligotrophic Water

Inveryclear-water,openoceanregions(Chl <0.1),weexpectminimalvariabilityingreenspectralbands.

R2018showsimprovedstabilityintemporaltrendsofwater-leavingreflectanceinthegreenbandsofMODIS/Aqua.

R2014.0

R2014.0

R2018.0

R2018.0R

rs(5

47)

Rrs

(555

)

instrumentcalibrationupdate

biasshift+3-5%inRrs

biasshift-9%inChl

RrsR

atio

Rrs(sr

-1)

ChlRatio

Chl(mgm

-3)

VIIRS/SNPP Global Deep-Water (R2018.0 vs R2014.0)

InstrumentCalibrationupdatesinclude:

• revisedtemporalcalwithadditionalsolar&lunarmeasurementssinceR2014

• lunarmeasurementscorrectedfordetectorgainvariability

• absolutecalibrationofallspectralbandsbasedonobservationsofthesunthroughthesolardiffuser

VIIRS/SNPP R2018.0 Reprocessingimproved agreement with in situ

Rrs(412) Rrs(443) Chlorophyll

R2014.0

R2018.0

Bias

Bias=5%Bias=-0.00032 Bias=+0.00001

VIIRS/SNPP R2018.0 Reprocessingimproved agreement with in situ

Rrs(412) Rrs(443) Chlorophyll

R201

4.0

R201

8.0

Bias

Bias = 5%Bias = -0.00032 Bias = +0.00001

Bias=-0.001

MODISA R2018.0 Reprocessingimproved agreement with in situ

R2014.0

R2018.0

Rrs(412) Rrs(443)

Bias

Chlorophyll

Bias=32%

Bias=18%

Bias=-0.00077

Bias=+0.00005 Bias=+0.00008

Bias=-0.00027

In situ Rrs Match-up Statistics for MODISA

Product NumberMatchups

MeanBias(sr-1) MeanAbsoluteError(sr-1)

R2014.0 R2018.0 R2014.0 R2018.0Rrs(412) 3934 -0.00077 0.00005 0.00125 0.00099

Rrs(443) 4134 -0.00027 0.00008 0.00082 0.00075

Rrs(488) 3772 -0.00067 -0.00050 0.00088 0.00077

Rrs(531) 2076 -0.00063 -0.00057 0.00083 0.00079

Rrs(547) 3619 -0.00052 -0.00048 0.00078 0.00076

Rrs(555) 3534 -0.00081 -0.00075 0.00096 0.00090

Rrs(667) 3573 -0.00017 -0.00016 0.00030 0.00029

Rrs(678) 472 -0.00016 -0.00014 0.00034 0.00033

• equalorreducedmeanbiasandMAEinallbands• largenegativebiasinbluereducedtosmallpositivebias

identicalfielddatasourcesfromAERONET-OCandSeaBASS

0.10

0.12

0.14

0.16

0.18

0.20

Chla

(mg

m−3)

0.10

0.12

0.14

0.16

0.18

0.20

Chla

(mg

m−3)

Jan

1998

Jan

2002

Jan

2006

Jan

2010

Jan

2014

−20

0

20

40

Chla

(%)

−0.04

−0.02

0.00

0.02

0.04Ch

la A

nom

aly

(mg

m−3)

−0.04

−0.02

0.00

0.02

0.04Ch

la A

nom

aly

(mg

m−3)

Jan

1998

Jan

2002

Jan

2006

Jan

2010

Jan

2014

−20

−10

0

10

20

Chla

Ano

mal

y (%

)

a.

b.

SeaWiFS MODISA VIIRS

Multivariate Enso Index (MEI)

SeaWiFS MODISA VIIRS

21-year Multi-mission Chlorophyll TimeseriesDeep-Water, +/- 40 deg latitude

• MODISAingoodagreementwithSeaWiFS andVIIRS(through~2015)

• anomalytrendsgenerallyconsistentwithexpectationsbasedonMEI

• suspectlatemissiontrendinVIIRS(didnotrecoverfrom2015/16ENSOevent)

• likelyissueisunresolvedimpactsinbluebandstochangingspectralresponseofVIIRS(TBD)

Chloroph

yll(mgm

-3)

Chloroph

yllA

nomaly(m

gm

-3)

from Ibrahim et al., Ocean Optics XXIV, October 2018, Dubrovnik, Croatia.

Gordon and Wang, 1994

MSE MBAC

SSàMS

AC Algorithm Refinement

TOAradiance

Lt

Rayleigh,gas,andglintcorrection

Estimate the optical depth at the longest high performance band

Estimate the aerosol spectral reflectance for a set of models

Lrgc

𝛁𝝌𝟐minimization

Averagebestmodels,calculatetransmittances

Rrs ,𝝉a,Angstrom

Multi-band Atmospheric Correction (MBAC)

MBAC Cost Function

observed/predictedaerosolreflectance adaptivespectralweight

radiometricuncertainty

SensorWavelength(l,nm)

MODIS 748 859 869 1240 1640 2130

VIIRS 746 868 1238 1604 2258

PACE-OCI 750 860 870 1038 1250 1615 2130 2260

𝑆𝑊(𝜆)

0

1

NIRiterations(Baileyetal.,2010)increasingwatercomplexity

Operational MBACChesapeake Bay

Pamlico Sound

Operational MBAC

• MBACchlorophyllretrievalssimilartostandardalgorithm,exceptinturbidcoastalregions.

• Differenceisduetoretrievedaerosolmodels(aerosoltype).

Application to MODISA (MBAC vs Std AC)

MODISA In Situ Match-up Analysis (MBAC vs Std AC)

SeaBASS in-situ matchups to

water-leaving reflectance shows

reduction in error using MBAC

relative to standard algorithm, at

all spectral bands.

Artifacts ?

Highly turbid example for upper

Chesapeake Bay, MBAC shows

more meaningful (positive) Rrs

retrievals in blue channels.

• Algorithm

• Sensor Random Noise

• Sensor Systematic Noiseuncertainty

𝐿+

RetrievalAlgorithm(InverseProblem)

uncertainty

bias

𝑅-.

observations retrievals

Development of a Per-Pixel Rrs(l) Uncertainty Product

from Ibrahim et al., Ocean Optics XXIV, October 2018, Dubrovnik, Croatia.

Uncertainty per-pixel

TOA measurements noise

Step-by-steperrorpropagation

Error Propagation

Sensor Random Noise

• SensornoisecanbepropagatedthroughtheMSE/MBACalgorithmsusingstandarderrorpropagation.

• ResultsagreeverywellwithMCsimulations(presentedpreviously).

MC Simulation

𝜃0123

𝜃.45 = 10° 𝜃.45 = 20°

𝜃.45 = 30° 𝜃.45 = 60°

𝜃0123

𝜃0123

𝜃0123

Simulationparameters:• 𝜃0123 = 0°: 80°,• ∆𝜑 = 0°: 180°,• 𝜃.45 = 10°, 20°, 30°, 60°• 𝜏D = 0.05: 0.35• RH = 77.5% à Not in the LUT• 𝑓 = 0: 0.95• chlor-a=0.03mg/m3

• total#cases:1037400

• average∆𝜌3 443 =0.0015

• uncertaintyincreaseswithsolar

zenithangle

• uncertaintyincreasessignificantly

forlargerfine-modefractioncases

duetothelinear-mixinglimitations

𝝀 = 𝟒𝟒𝟑𝒏𝒎

Algorithm Uncertainty

𝜃0123

𝑅𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛𝑖𝑛∆𝜌3𝑤𝑖𝑡ℎ𝑀𝐵𝐴𝐶

𝜃0123 𝜃0123

Simulationparameters:• classicalcovarianceanalysis• assumecorrelatedbiasof2%onbands>750nmand0.5%onVNIRbands

• assume10%correlatedcovariancebetweenbands

average uncertainty:

• Operational 2-band AC à 0.0036

• MODIS-MBAC 6-band à 0.0024

• Uncertainty is reduced by à 34%

𝝀 = 𝟒𝟒𝟑𝒏𝒎, 𝜽𝒔𝒖𝒏 = 𝟑𝟎°

𝑀𝐵𝐴𝐶 𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝐴𝑙𝑔𝑜𝑟𝑖𝑡ℎ𝑚

Sensor Systematic Error

Future DevelopmentMulti-BandAtmosphericCorrection• assess/improvespectral-relativecalibrationforNIR/SWIRregion(MODIS&VIIRS)• assess/improveperformanceofadaptiveweightingscheme• initiateglobal-scaletestingforMODIS&VIIRS

Rrs UncertaintyProduct• completethealgorithmframeworkforRrs uncertaintyproduct(combined

random/systematic/algorithmerror)• refinesystematicerrortermsforeachinstrument• assessuncertaintyestimatesrelativetomatch-upresults(closureexperiment)

InstrumentCalibration• reassessVIIRSspectralresponsechanges,impactontrendsinblue• evaluateMODIS-TerraR2018.0results